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Big Data Exploration

Energy analytics for access, efficiency and development

Anna Lerner's picture
Image from Chris Chopyak, who captured the workshop in
simple designs and strategic illustrations
What do Open and Big Data principles and advanced analytics have to do with energy access and efficiency? A lot. At a recent workshop, we explored a range of challenges and solutions alongside experts from the U.S. Department of Energy, the University of Chicago and other organizations.
 
Today, about 1.1 billion people around the world live without electricity. Cities, which now house more than half the world’s population, struggle under the weight of inefficient, expensive and often-polluting energy systems. Energy access and affordability are paramount in addressing poverty alleviation and shared prosperity goals, and cleaner energy is critical in mitigating climate change.
 
Applications of Open and Big Data principles and advanced analytics is an area of innovation that can help address many pressing energy sector challenges in the developing world, as well as provide social and financial dividends at low cost.

The World Bank Group is committed to accelerating the use of Open Data and advanced analytics to improve access to reliable, affordable and sustainable electricity, in line with its commitment to the Sustainable Energy for All (SE4ALL) initiative. In order to increase awareness around opportunities of new data capturing and analyzing solutions in the energy sector in emerging markets, the World Bank Group and University of Chicago hosted a training session and a subsequent workshop in mid-May.

A real-time food security information system is a Big Data reality

John Corbett's picture
Today, all the necessary information assets are available to provide actionable insight to farmers: models, real-time local weather, crop and agronomic insight and calendars. What is also emerging is the technology to deliver actionable insights directly into the hands of farmers: information and communication technologies (ICTs) including cell phones, tablets and other personal communication tools.   

​With a cell phone in hand, a farmer becomes connected to a network of invaluable – and timely – information. There is greater demand for information as extreme weather variability necessitates new farming practices. Local and timely insights help inform farmer decisions. Big Data methods and practices, meanwhile, ensure that this multi-directional information contributes across the agricultural value chain as input providers and produce buyers are also informed.

The warming of the atmosphere is leading to a tremendous increase in weather variability. This variability affects agriculture in a multitude of ways and most insidiously for farmers, in the uncertainty that impacts each step in their production and livelihoods. 

The most common human reaction to uncertain times is to become more risk averse.  For our planet’s 570+ million small-holder farmers, this means lower productivity. With the impeding population surge, particularly in Africa, and diet changes requiring “70 percent more food production,” change must come now.

How does accessibility re-frame our projects?

Tatiana Peralta Quiros's picture
The increasing availability of standardized transport data and computing power is allowing us to understand the spatial and network impacts of different transportation projects or policies. In January, we officially introduced the OpenTripPlannerAnalyst (OTPA) Accessibility Tool. This open-source web-based tool allows us to combine the spatial distribution of the city (for example, jobs or schools), the transportation network and an individual’s travel behavior to calculate the ease with which an individual can access opportunities.

Using the OTPA Accessibility tool, we are unlocking the potential of these data sets and analysis techniques for modeling block-level accessibility. This tool allows anyone to model the interplay of transportation and land use in a city, and the ability to design transportation services that more accurately address citizens’ needs – for instance, tailored services connecting the poor or the bottom 40 percent to strategic places of interest.

In just a couple of months, we have begun to explore the different uses of the tool, and how it can be utilized in an operational context to inform our projects.
 
Employment Accessibility Changes in Lima,
Metro Line 2. TTL: Georges Darido

Comparing transportation scenarios
The most obvious use of the tool is to compare the accessibility impacts of different transportation networks. The tool allows users to upload different transportation scenarios, and compare how the access to jobs changes in the different parts of the city. In Lima, Peru, we were able to compare the employment accessibility changes that were produced by adding a new metro line. It also helped us understand the network and connectivity impacts of the projects, rather than relying on only travel times.

Understanding spatial form
However, the tool’s uses are not limited to comparing transport scenarios. Combining the tool with earth observation data to identify the location of slums and social housing, we are to explore the spatial form of a city and the accessibility opportunities that are provided to a city’s most vulnerable population.  We did so in Buenos Aires, Argentina, were we combined LandScan data and outputs from the tool to understand the employment accessibility options available to the city’s poorest population groups.

What does Big Data have to do with an owl?

Nak Moon Sung's picture
This is the story of an owl, but not any owl. This owl is from Seoul and it came into existence thanks to Big Data. How come, you may ask? Well, read on to find out.
 
 Meet your new friend: the owl bus

Officials in Seoul had long searched for a transport system for low-income workers who commute late at night. Although a taxi ride was an option, it was a very pricey one, particularly for a commute on a regular basis. Low-income workers do not make enough money to take a taxi regularly, and taxi fares are considerably higher at night. Furthermore, since low-income workers tend to live on the outskirts of the city, taxi drivers often are reluctant to go there mainly for distance and security reasons. 

These were some of the big challenges faced by policy makers in Seoul, a city regarded as a champion of public transportation. So what to do?

Part of the solution was the analysis and utilization of Big Data to come up with a suitable mode of transport that would serve the specific needs of late-night workers. The result was the creation of the “owl bus,” which operates late into the night until five o’clock in the morning.

In this context, Big Data has a considerable potential application in the transport sector, and for infrastructure development in general. In fact, World Bank and Korean officials will discuss on Tuesday, May 28 the theme “Leveraging Information Communication Technologies (ICT) in transport for greener growth and smarter development.”

Ending Poverty and "Factivism"

Maya Brahmam's picture

Can factivism push us closer to the edge of ending extreme poverty? This was the subject of Bono’s latest TED Talk on ending poverty. Simply put, according to Bono, technology can help us end extreme poverty in a variety of ways, from creating new drugs for AIDS to empowering people via openness and transparency. And numbers from the World Bank’s Research Group show this shift: 22% of the developing world’s population – or 1.29 billion people – lived on $1.25 or less a day in 2008, down from 43% in 1990 and 523% in 1981.

So how do we accelerate this progress? One answer may be in moving the focus to empowering people to develop their own solutions using new technologies and using data to make better decisions. We’re hoping that the Data Dives for our “Big Data Exploration” this weekend—being done jointly with UNDP, Global Pulse, and Qatar Computing Research Institute – will help us get a little bit closer to solving larger development questions. This pilot will explore whether the Bank and other development organizations can use big data to deliver better operational results and increase development effectiveness.